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Information Journal Paper

Title

Diagnosis and classification of Alzheimer's disease by 3D Residual Block

Pages

  1748-1755

Abstract

 Introduction: In recent years, many studies have been done to analyze brain diseases in order to identify brain diseases that have a significant role in creating diagnostic intelligent systems. among the different methods of machine learning, Deep learning based methods in recent years have been a wide application of the development of intelligent systems, which resulted in the creation of powerful systems for diagnosis of disease. Materials and Methods: In this study, the diagnosis of Alzheimer's patients with Deep learning neural network is based on method of 3-Dimensional Residual Block. the training and test procedure presented by ADNI data set. Results: The results showed that the output of this Method were conducted in comparison to the proposed methods, accuracy of diagnose and classification of Alzheimer's disease. Conclusion: the findings of the present study showed that the machine learning with Deep learning methods can diagnose Alzheimer's disease sooner than doctors.

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    Cite

    APA: Copy

    Razavi, Firouzeh, TAROKH, MOHAMMAD JAFAR, & ALBORZI, MAHMOOD. (2020). Diagnosis and classification of Alzheimer's disease by 3D Residual Block. MEDICAL JOURNAL OF MASHHAD UNIVERSITY OF MEDICAL SCIENCES, 62(5 ), 1748-1755. SID. https://sid.ir/paper/51358/en

    Vancouver: Copy

    Razavi Firouzeh, TAROKH MOHAMMAD JAFAR, ALBORZI MAHMOOD. Diagnosis and classification of Alzheimer's disease by 3D Residual Block. MEDICAL JOURNAL OF MASHHAD UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2020;62(5 ):1748-1755. Available from: https://sid.ir/paper/51358/en

    IEEE: Copy

    Firouzeh Razavi, MOHAMMAD JAFAR TAROKH, and MAHMOOD ALBORZI, “Diagnosis and classification of Alzheimer's disease by 3D Residual Block,” MEDICAL JOURNAL OF MASHHAD UNIVERSITY OF MEDICAL SCIENCES, vol. 62, no. 5 , pp. 1748–1755, 2020, [Online]. Available: https://sid.ir/paper/51358/en

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